Overview

Dataset statistics

Number of variables24
Number of observations3586
Missing cells1647
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory829.4 KiB
Average record size in memory236.8 B

Variable types

Numeric16
Categorical8

Alerts

title has a high cardinality: 3119 distinct valuesHigh cardinality
body has a high cardinality: 2051 distinct valuesHigh cardinality
datetime has a high cardinality: 3120 distinct valuesHigh cardinality
author has a high cardinality: 2813 distinct valuesHigh cardinality
url has a high cardinality: 3120 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with monthHigh correlation
score is highly overall correlated with upvote_ratio and 2 other fieldsHigh correlation
upvote_ratio is highly overall correlated with score and 2 other fieldsHigh correlation
num_questions_body is highly overall correlated with body_lenHigh correlation
month is highly overall correlated with Unnamed: 0High correlation
body_len is highly overall correlated with num_questions_bodyHigh correlation
tag_index is highly overall correlated with tagHigh correlation
upvote_bins is highly overall correlated with score and 2 other fieldsHigh correlation
score_bins is highly overall correlated with score and 2 other fieldsHigh correlation
tag is highly overall correlated with tag_indexHigh correlation
author_kind is highly imbalanced (75.4%)Imbalance
bold_B is highly imbalanced (99.6%)Imbalance
body has 1531 (42.7%) missing valuesMissing
tag has 116 (3.2%) missing valuesMissing
edit is highly skewed (γ1 = 42.21950457)Skewed
title is uniformly distributedUniform
body is uniformly distributedUniform
datetime is uniformly distributedUniform
url is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
num_questions_body has 1531 (42.7%) zerosZeros
num_cite has 3342 (93.2%) zerosZeros
body_len has 1531 (42.7%) zerosZeros
tag_index has 412 (11.5%) zerosZeros
edit has 3560 (99.3%) zerosZeros
nice has 3368 (93.9%) zerosZeros
upvote_bins has 147 (4.1%) zerosZeros
score_bins has 361 (10.1%) zerosZeros

Reproduction

Analysis started2023-08-07 07:14:18.655300
Analysis finished2023-08-07 07:14:54.973752
Duration36.32 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3586
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2065.8338
Minimum0
Maximum4203
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:55.085845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile184.25
Q1926.25
median1980.5
Q33222.75
95-th percentile4000.75
Maximum4203
Range4203
Interquartile range (IQR)2296.5

Descriptive statistics

Standard deviation1268.7694
Coefficient of variation (CV)0.61416819
Kurtosis-1.3424442
Mean2065.8338
Median Absolute Deviation (MAD)1148
Skewness0.047872249
Sum7408080
Variance1609775.8
MonotonicityNot monotonic
2023-08-07T00:14:55.245775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
285 1
 
< 0.1%
4123 1
 
< 0.1%
1102 1
 
< 0.1%
643 1
 
< 0.1%
699 1
 
< 0.1%
481 1
 
< 0.1%
2410 1
 
< 0.1%
42 1
 
< 0.1%
1362 1
 
< 0.1%
1441 1
 
< 0.1%
Other values (3576) 3576
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4203 1
< 0.1%
4202 1
< 0.1%
4201 1
< 0.1%
4200 1
< 0.1%
4199 1
< 0.1%
4198 1
< 0.1%
4197 1
< 0.1%
4196 1
< 0.1%
4195 1
< 0.1%
4194 1
< 0.1%

title
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3119
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
If hand sanitizer kills 99.99% of germs, then won't the surviving 0.01% make hand sanitizer resistant strains?
 
3
What happens to water when it freezes and can't expand?
 
2
Are humans closer in relative size to the planck length or the entire observable universe?
 
2
What is the ecological impact of toilet paper?
 
2
Humans seem to have a universally visceral reaction of disgust when seeing most insects and spiders. Do other animal species have this same reaction?
 
2
Other values (3114)
3575 

Length

Max length299
Median length223
Mean length96.863079
Min length14

Characters and Unicode

Total characters347351
Distinct characters104
Distinct categories15 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2653 ?
Unique (%)74.0%

Sample

1st rowWhere in your body does your food turn brown?
2nd rowIs the Omicron variant affecting the heart like the earlier COVID variants were? Are cardiac effects less likely if one is vaccinated?
3rd rowWhy does the water pull back before a tsunami?
4th rowWhy do isotopes have a fractional atomic mass?
5th rowWolfram Alpha says that the UK's life expectancy for men is 77 years. It then says that the chance of living past 80 is 51%. So if most people will live past 80, how can the life expectancy be less?

Common Values

ValueCountFrequency (%)
If hand sanitizer kills 99.99% of germs, then won't the surviving 0.01% make hand sanitizer resistant strains? 3
 
0.1%
What happens to water when it freezes and can't expand? 2
 
0.1%
Are humans closer in relative size to the planck length or the entire observable universe? 2
 
0.1%
What is the ecological impact of toilet paper? 2
 
0.1%
Humans seem to have a universally visceral reaction of disgust when seeing most insects and spiders. Do other animal species have this same reaction? 2
 
0.1%
Is there a reason all the planets orbit the sun in approximately the same plane and direction? 2
 
0.1%
Why can hormone therapy make a clitoris grow but can't make a penis grow? 2
 
0.1%
COVID-19 started with one person getting infected and spread globally: doesn't that mean that as long as there's at least one person infected, there is always the risk of it spiking again? Even if only one person in America is infected, can't that person be the catalyst for another epidemic? 2
 
0.1%
Do giraffes get struck by lightning more often than other animals? 2
 
0.1%
Why are Garlic and Onions Poisonous to Dogs and Cats and Not To Humans? 2
 
0.1%
Other values (3109) 3565
99.4%

Length

2023-08-07T00:14:55.418175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 2894
 
4.7%
a 1655
 
2.7%
of 1433
 
2.3%
to 1388
 
2.3%
is 1325
 
2.2%
in 975
 
1.6%
and 950
 
1.6%
how 884
 
1.4%
do 829
 
1.4%
why 784
 
1.3%
Other values (7764) 47941
78.5%

Most occurring characters

ValueCountFrequency (%)
57488
16.6%
e 34689
 
10.0%
t 24309
 
7.0%
a 22485
 
6.5%
o 21825
 
6.3%
i 18805
 
5.4%
s 18399
 
5.3%
n 18340
 
5.3%
r 16470
 
4.7%
h 13311
 
3.8%
Other values (94) 101230
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 270843
78.0%
Space Separator 57488
 
16.6%
Uppercase Letter 8225
 
2.4%
Other Punctuation 7849
 
2.3%
Decimal Number 1551
 
0.4%
Dash Punctuation 458
 
0.1%
Close Punctuation 294
 
0.1%
Open Punctuation 294
 
0.1%
Final Punctuation 204
 
0.1%
Math Symbol 63
 
< 0.1%
Other values (5) 82
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34689
12.8%
t 24309
 
9.0%
a 22485
 
8.3%
o 21825
 
8.1%
i 18805
 
6.9%
s 18399
 
6.8%
n 18340
 
6.8%
r 16470
 
6.1%
h 13311
 
4.9%
l 11664
 
4.3%
Other values (21) 70546
26.0%
Uppercase Letter
ValueCountFrequency (%)
W 1440
17.5%
I 1260
15.3%
H 796
9.7%
D 729
8.9%
A 726
8.8%
C 549
 
6.7%
S 435
 
5.3%
M 252
 
3.1%
E 234
 
2.8%
O 225
 
2.7%
Other values (16) 1579
19.2%
Other Punctuation
ValueCountFrequency (%)
? 4051
51.6%
, 1626
20.7%
' 662
 
8.4%
. 605
 
7.7%
" 440
 
5.6%
/ 249
 
3.2%
% 64
 
0.8%
: 64
 
0.8%
! 47
 
0.6%
& 15
 
0.2%
Other values (3) 26
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 433
27.9%
1 292
18.8%
2 183
11.8%
9 180
11.6%
5 109
 
7.0%
3 98
 
6.3%
4 87
 
5.6%
8 64
 
4.1%
6 57
 
3.7%
7 48
 
3.1%
Math Symbol
ValueCountFrequency (%)
+ 27
42.9%
~ 13
20.6%
= 12
19.0%
> 4
 
6.3%
| 4
 
6.3%
2
 
3.2%
< 1
 
1.6%
Final Punctuation
ValueCountFrequency (%)
153
75.0%
50
 
24.5%
» 1
 
0.5%
Initial Punctuation
ValueCountFrequency (%)
51
82.3%
10
 
16.1%
« 1
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 455
99.3%
3
 
0.7%
Close Punctuation
ValueCountFrequency (%)
) 276
93.9%
] 18
 
6.1%
Open Punctuation
ValueCountFrequency (%)
( 276
93.9%
[ 18
 
6.1%
Space Separator
ValueCountFrequency (%)
57488
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 10
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 6
100.0%
Other Symbol
ValueCountFrequency (%)
° 2
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 279065
80.3%
Common 68283
 
19.7%
Greek 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34689
12.4%
t 24309
 
8.7%
a 22485
 
8.1%
o 21825
 
7.8%
i 18805
 
6.7%
s 18399
 
6.6%
n 18340
 
6.6%
r 16470
 
5.9%
h 13311
 
4.8%
l 11664
 
4.2%
Other values (44) 78768
28.2%
Common
ValueCountFrequency (%)
57488
84.2%
? 4051
 
5.9%
, 1626
 
2.4%
' 662
 
1.0%
. 605
 
0.9%
- 455
 
0.7%
" 440
 
0.6%
0 433
 
0.6%
1 292
 
0.4%
) 276
 
0.4%
Other values (37) 1955
 
2.9%
Greek
ValueCountFrequency (%)
μ 1
33.3%
σ 1
33.3%
π 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 347072
99.9%
Punctuation 267
 
0.1%
None 10
 
< 0.1%
Math Operators 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57488
16.6%
e 34689
 
10.0%
t 24309
 
7.0%
a 22485
 
6.5%
o 21825
 
6.3%
i 18805
 
5.4%
s 18399
 
5.3%
n 18340
 
5.3%
r 16470
 
4.7%
h 13311
 
3.8%
Other values (80) 100951
29.1%
Punctuation
ValueCountFrequency (%)
153
57.3%
51
 
19.1%
50
 
18.7%
10
 
3.7%
3
 
1.1%
None
ValueCountFrequency (%)
é 2
20.0%
° 2
20.0%
μ 1
10.0%
σ 1
10.0%
π 1
10.0%
» 1
10.0%
« 1
10.0%
ö 1
10.0%
Math Operators
ValueCountFrequency (%)
2
100.0%

body
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct2051
Distinct (%)99.8%
Missing1531
Missing (%)42.7%
Memory size56.0 KiB
&#x200B;
 
2
Just curious.
 
2
 
2
Edit: First gold, thank you kind stranger.
 
2
I know this is maybe a stupid question, but poop is brown, but when you throw up your throw up is just the color of your food. Where does your body make your food brown? (Sorry for my crappy English) Edit: Thank you guys so much for the anwers and thanks dor the gold. This post litteraly started by a friend and me just joking around. Thanks
 
1
Other values (2046)
2046 

Length

Max length5134
Median length777
Mean length420.57762
Min length1

Characters and Unicode

Total characters864287
Distinct characters141
Distinct categories21 ?
Distinct scripts4 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2047 ?
Unique (%)99.6%

Sample

1st rowI know this is maybe a stupid question, but poop is brown, but when you throw up your throw up is just the color of your food. Where does your body make your food brown? (Sorry for my crappy English) Edit: Thank you guys so much for the anwers and thanks dor the gold. This post litteraly started by a friend and me just joking around. Thanks
2nd rowAs the question asks, why do specific isotopes not have a whole number atomic mass? For instance, Ag-109 has an atomic mass of 108.9047558 amu and not just 109. Or Ag-107 has a mass of 106.9050915 amu.
3rd rowSource: http://www.wolframalpha.com/input/?i=uk+life+expectancy+for+men&dataset=
4th rowHow do chemists predict how a chemical reaction or product will behave, I understand how and why molecules combine themselves in the structures they end up in (like H2O for example) I also understand things like the acidity, charge and reactivity of the different elements. But the thing I can't wrap my head around is how we know about the ways a certain molecule will behave, take for example table salt, it is composed of 2 individually dangerous elements but the molecule they produce in this case is completely harmless. So how do chemists predict how these compositions behave. I am a aspiring material / composite engineer (mostly self taught)
5th rowIf the force put on us by the spinning Earth is similar to that of a centrifuge, is the gravitational force we feel coming from that spinning, or the mass of the Earth itself?

Common Values

ValueCountFrequency (%)
&#x200B; 2
 
0.1%
Just curious. 2
 
0.1%
2
 
0.1%
Edit: First gold, thank you kind stranger. 2
 
0.1%
I know this is maybe a stupid question, but poop is brown, but when you throw up your throw up is just the color of your food. Where does your body make your food brown? (Sorry for my crappy English) Edit: Thank you guys so much for the anwers and thanks dor the gold. This post litteraly started by a friend and me just joking around. Thanks 1
 
< 0.1%
Just as the title says, how many average sized male humans would it take to make an average black hole. 1
 
< 0.1%
Assuming all life started from one cell, the DNA in that cell would get replicated after each division. However, one of the new cells would still contain the "original" DNA. Would this original still exist today? Or would mutations have gotten rid of the original strand/chromosome a while ago? 1
 
< 0.1%
Isnt bacterials love to eat sugar ? so what is the mechanism here guys ? 1
 
< 0.1%
I am assuming, since we currently think the universe is expanding, that the centre of the universe is where it all began. Does anyone know where it is and can we get there? 1
 
< 0.1%
I almost never remember my dreams. For example if I remember 1 a year it's a big deal. This past month I've been on a super restrictive diet, (trying to lose the COVID kilos). However a couple of times I've had a regular meal and then when I go to sleep I have super vivid dreams that I can remember when I wake up. I am wondering if there is any link between a gut biome and dreaming? As lately my gut bacteria has been cut off from its regular sugars and carbs and processed food that it was used to. And then when having a little bit of it back i have dreamed vividly. (I've been on my restrictive diet for a 5 weeks now and have had 3 cheat meals during this time which have each triggered amazing dreams) 1
 
< 0.1%
Other values (2041) 2041
56.9%
(Missing) 1531
42.7%

Length

2023-08-07T00:14:55.608166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 6831
 
4.7%
to 3592
 
2.5%
a 3397
 
2.4%
of 3370
 
2.3%
and 3072
 
2.1%
is 2654
 
1.8%
i 2400
 
1.7%
that 2279
 
1.6%
in 2206
 
1.5%
it 2055
 
1.4%
Other values (13998) 112263
77.9%

Most occurring characters

ValueCountFrequency (%)
140985
16.3%
e 81317
 
9.4%
t 63298
 
7.3%
a 53048
 
6.1%
o 51380
 
5.9%
i 47747
 
5.5%
n 45504
 
5.3%
s 45440
 
5.3%
r 38501
 
4.5%
h 32066
 
3.7%
Other values (131) 265001
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 660670
76.4%
Space Separator 141005
 
16.3%
Other Punctuation 25239
 
2.9%
Uppercase Letter 19095
 
2.2%
Decimal Number 6132
 
0.7%
Control 4856
 
0.6%
Dash Punctuation 2238
 
0.3%
Close Punctuation 1762
 
0.2%
Open Punctuation 1699
 
0.2%
Final Punctuation 527
 
0.1%
Other values (11) 1064
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 81317
12.3%
t 63298
 
9.6%
a 53048
 
8.0%
o 51380
 
7.8%
i 47747
 
7.2%
n 45504
 
6.9%
s 45440
 
6.9%
r 38501
 
5.8%
h 32066
 
4.9%
l 27636
 
4.2%
Other values (31) 174733
26.4%
Uppercase Letter
ValueCountFrequency (%)
I 4586
24.0%
A 1636
 
8.6%
T 1522
 
8.0%
S 1211
 
6.3%
W 1116
 
5.8%
D 896
 
4.7%
E 825
 
4.3%
C 823
 
4.3%
H 758
 
4.0%
M 709
 
3.7%
Other values (18) 5013
26.3%
Other Punctuation
ValueCountFrequency (%)
. 6555
26.0%
, 5815
23.0%
/ 3168
12.6%
? 3091
12.2%
' 2406
 
9.5%
: 1244
 
4.9%
* 1064
 
4.2%
" 865
 
3.4%
! 377
 
1.5%
% 185
 
0.7%
Other values (8) 469
 
1.9%
Decimal Number
ValueCountFrequency (%)
0 1386
22.6%
1 1161
18.9%
2 1018
16.6%
5 463
 
7.6%
3 458
 
7.5%
9 389
 
6.3%
4 385
 
6.3%
6 347
 
5.7%
8 272
 
4.4%
7 253
 
4.1%
Math Symbol
ValueCountFrequency (%)
= 152
35.9%
+ 100
23.6%
> 68
16.1%
~ 53
 
12.5%
| 43
 
10.2%
< 6
 
1.4%
1
 
0.2%
Other Number
ValueCountFrequency (%)
² 7
28.0%
6
24.0%
6
24.0%
4
16.0%
³ 2
 
8.0%
Open Punctuation
ValueCountFrequency (%)
( 1310
77.1%
[ 385
 
22.7%
3
 
0.2%
{ 1
 
0.1%
Other Symbol
ValueCountFrequency (%)
° 8
66.7%
😊 2
 
16.7%
😭 1
 
8.3%
® 1
 
8.3%
Control
ValueCountFrequency (%)
4842
99.7%
10
 
0.2%
4
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 2202
98.4%
24
 
1.1%
12
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 1376
78.1%
] 385
 
21.9%
} 1
 
0.1%
Space Separator
ValueCountFrequency (%)
140985
> 99.9%
  20
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
456
86.5%
71
 
13.5%
Initial Punctuation
ValueCountFrequency (%)
71
86.6%
11
 
13.4%
Modifier Symbol
ValueCountFrequency (%)
^ 45
95.7%
` 2
 
4.3%
Modifier Letter
ValueCountFrequency (%)
ː 3
50.0%
ˈ 3
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 463
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 3
100.0%
Format
ValueCountFrequency (%)
­ 1
100.0%
Other Letter
ValueCountFrequency (%)
ʔ 1
100.0%
Nonspacing Mark
ValueCountFrequency (%)
͗ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 679756
78.6%
Common 184520
 
21.3%
Greek 10
 
< 0.1%
Inherited 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
140985
76.4%
. 6555
 
3.6%
, 5815
 
3.2%
4842
 
2.6%
/ 3168
 
1.7%
? 3091
 
1.7%
' 2406
 
1.3%
- 2202
 
1.2%
0 1386
 
0.8%
) 1376
 
0.7%
Other values (60) 12694
 
6.9%
Latin
ValueCountFrequency (%)
e 81317
12.0%
t 63298
 
9.3%
a 53048
 
7.8%
o 51380
 
7.6%
i 47747
 
7.0%
n 45504
 
6.7%
s 45440
 
6.7%
r 38501
 
5.7%
h 32066
 
4.7%
l 27636
 
4.1%
Other values (54) 193819
28.5%
Greek
ValueCountFrequency (%)
β 3
30.0%
α 2
20.0%
ϵ 2
20.0%
γ 1
 
10.0%
δ 1
 
10.0%
Ψ 1
 
10.0%
Inherited
ValueCountFrequency (%)
͗ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 863527
99.9%
Punctuation 667
 
0.1%
None 78
 
< 0.1%
Modifier Letters 6
 
< 0.1%
Emoticons 3
 
< 0.1%
IPA Ext 2
 
< 0.1%
Latin Ext Additional 1
 
< 0.1%
Math Operators 1
 
< 0.1%
Alphabetic PF 1
 
< 0.1%
Diacriticals 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
140985
16.3%
e 81317
 
9.4%
t 63298
 
7.3%
a 53048
 
6.1%
o 51380
 
6.0%
i 47747
 
5.5%
n 45504
 
5.3%
s 45440
 
5.3%
r 38501
 
4.5%
h 32066
 
3.7%
Other values (88) 264241
30.6%
Punctuation
ValueCountFrequency (%)
456
68.4%
71
 
10.6%
71
 
10.6%
24
 
3.6%
13
 
1.9%
12
 
1.8%
11
 
1.6%
6
 
0.9%
3
 
0.4%
None
ValueCountFrequency (%)
  20
25.6%
° 8
 
10.3%
² 7
 
9.0%
6
 
7.7%
6
 
7.7%
4
 
5.1%
é 4
 
5.1%
β 3
 
3.8%
α 2
 
2.6%
³ 2
 
2.6%
Other values (14) 16
20.5%
Modifier Letters
ValueCountFrequency (%)
ː 3
50.0%
ˈ 3
50.0%
Emoticons
ValueCountFrequency (%)
😊 2
66.7%
😭 1
33.3%
Latin Ext Additional
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
IPA Ext
ValueCountFrequency (%)
ʔ 1
50.0%
ʕ 1
50.0%
Alphabetic PF
ValueCountFrequency (%)
1
100.0%
Diacriticals
ValueCountFrequency (%)
͗ 1
100.0%

tag
Categorical

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)0.8%
Missing116
Missing (%)3.2%
Memory size56.0 KiB
biology
639 
physics
532 
human body
412 
medicine
298 
earth sciences
278 
Other values (23)
1311 

Length

Max length27
Median length17
Mean length9.2184438
Min length4

Characters and Unicode

Total characters31988
Distinct characters26
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowhuman body
2nd rowcovid-19
3rd rowearth sciences
4th rowchemistry
5th rowmathematics

Common Values

ValueCountFrequency (%)
biology 639
17.8%
physics 532
14.8%
human body 412
11.5%
medicine 298
8.3%
earth sciences 278
7.8%
astronomy 231
 
6.4%
chemistry 196
 
5.5%
covid-19 175
 
4.9%
engineering 167
 
4.7%
planetary sci. 105
 
2.9%
Other values (18) 437
12.2%
(Missing) 116
 
3.2%

Length

2023-08-07T00:14:55.772601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
biology 641
14.9%
physics 532
12.4%
human 412
9.6%
body 412
9.6%
medicine 298
 
6.9%
earth 280
 
6.5%
sciences 280
 
6.5%
astronomy 231
 
5.4%
chemistry 196
 
4.6%
covid-19 175
 
4.1%
Other values (29) 834
19.4%

Most occurring characters

ValueCountFrequency (%)
i 3218
 
10.1%
o 2894
 
9.0%
e 2702
 
8.4%
s 2498
 
7.8%
c 2362
 
7.4%
y 2344
 
7.3%
n 2201
 
6.9%
h 1598
 
5.0%
a 1394
 
4.4%
m 1350
 
4.2%
Other values (16) 9427
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30537
95.5%
Space Separator 821
 
2.6%
Decimal Number 350
 
1.1%
Dash Punctuation 175
 
0.5%
Other Punctuation 105
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 3218
10.5%
o 2894
 
9.5%
e 2702
 
8.8%
s 2498
 
8.2%
c 2362
 
7.7%
y 2344
 
7.7%
n 2201
 
7.2%
h 1598
 
5.2%
a 1394
 
4.6%
m 1350
 
4.4%
Other values (11) 7976
26.1%
Decimal Number
ValueCountFrequency (%)
1 175
50.0%
9 175
50.0%
Space Separator
ValueCountFrequency (%)
821
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175
100.0%
Other Punctuation
ValueCountFrequency (%)
. 105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 30537
95.5%
Common 1451
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 3218
10.5%
o 2894
 
9.5%
e 2702
 
8.8%
s 2498
 
8.2%
c 2362
 
7.7%
y 2344
 
7.7%
n 2201
 
7.2%
h 1598
 
5.2%
a 1394
 
4.6%
m 1350
 
4.4%
Other values (11) 7976
26.1%
Common
ValueCountFrequency (%)
821
56.6%
- 175
 
12.1%
1 175
 
12.1%
9 175
 
12.1%
. 105
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 3218
 
10.1%
o 2894
 
9.0%
e 2702
 
8.4%
s 2498
 
7.8%
c 2362
 
7.4%
y 2344
 
7.3%
n 2201
 
6.9%
h 1598
 
5.0%
a 1394
 
4.4%
m 1350
 
4.2%
Other values (16) 9427
29.5%

datetime
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3120
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
2017-11-26 22:22:01
 
2
2017-10-07 08:57:33
 
2
2017-02-05 05:11:47
 
2
2018-11-11 07:17:42
 
2
2020-03-03 04:59:11
 
2
Other values (3115)
3576 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters68134
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2654 ?
Unique (%)74.0%

Sample

1st row2019-04-01 10:59:58
2nd row2022-02-07 14:39:54
3rd row2022-01-13 14:08:14
4th row2022-11-12 10:23:09
5th row2012-12-26 04:30:15

Common Values

ValueCountFrequency (%)
2017-11-26 22:22:01 2
 
0.1%
2017-10-07 08:57:33 2
 
0.1%
2017-02-05 05:11:47 2
 
0.1%
2018-11-11 07:17:42 2
 
0.1%
2020-03-03 04:59:11 2
 
0.1%
2017-01-03 03:52:48 2
 
0.1%
2019-06-15 03:53:21 2
 
0.1%
2020-07-15 10:50:00 2
 
0.1%
2017-03-29 08:25:25 2
 
0.1%
2020-09-29 01:37:16 2
 
0.1%
Other values (3110) 3566
99.4%

Length

2023-08-07T00:14:55.911794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-11-30 37
 
0.5%
2022-01-31 27
 
0.4%
2022-11-23 27
 
0.4%
2022-02-06 25
 
0.3%
2022-01-16 23
 
0.3%
2022-01-26 23
 
0.3%
2022-01-18 22
 
0.3%
2022-01-24 22
 
0.3%
2022-02-05 21
 
0.3%
2022-11-02 21
 
0.3%
Other values (4538) 6924
96.5%

Most occurring characters

ValueCountFrequency (%)
2 12487
18.3%
0 12296
18.0%
1 8953
13.1%
- 7172
10.5%
: 7172
10.5%
3586
 
5.3%
3 3297
 
4.8%
4 2903
 
4.3%
5 2842
 
4.2%
7 1963
 
2.9%
Other values (3) 5463
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50204
73.7%
Dash Punctuation 7172
 
10.5%
Other Punctuation 7172
 
10.5%
Space Separator 3586
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 12487
24.9%
0 12296
24.5%
1 8953
17.8%
3 3297
 
6.6%
4 2903
 
5.8%
5 2842
 
5.7%
7 1963
 
3.9%
8 1955
 
3.9%
9 1759
 
3.5%
6 1749
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 7172
100.0%
Other Punctuation
ValueCountFrequency (%)
: 7172
100.0%
Space Separator
ValueCountFrequency (%)
3586
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68134
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 12487
18.3%
0 12296
18.0%
1 8953
13.1%
- 7172
10.5%
: 7172
10.5%
3586
 
5.3%
3 3297
 
4.8%
4 2903
 
4.3%
5 2842
 
4.2%
7 1963
 
2.9%
Other values (3) 5463
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 12487
18.3%
0 12296
18.0%
1 8953
13.1%
- 7172
10.5%
: 7172
10.5%
3586
 
5.3%
3 3297
 
4.8%
4 2903
 
4.3%
5 2842
 
4.2%
7 1963
 
2.9%
Other values (3) 5463
8.0%

author
Categorical

Distinct2813
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
DELETED
 
188
AskScienceModerator
 
43
inquilinekea
 
8
Gargatua13013
 
6
Toorelad
 
6
Other values (2808)
3335 

Length

Max length20
Median length15
Mean length11.358896
Min length3

Characters and Unicode

Total characters40733
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2320 ?
Unique (%)64.7%

Sample

1st rowAyko03
2nd rowuoflcards22
3rd rowBiLeftHanded
4th rowMSPaintIsBetter
5th rowBriggykins

Common Values

ValueCountFrequency (%)
DELETED 188
 
5.2%
AskScienceModerator 43
 
1.2%
inquilinekea 8
 
0.2%
Gargatua13013 6
 
0.2%
Toorelad 6
 
0.2%
RichardsonM24 5
 
0.1%
Most-Ant2788 5
 
0.1%
AsAChemicalEngineer 5
 
0.1%
PHealthy 5
 
0.1%
jamx02 4
 
0.1%
Other values (2803) 3311
92.3%

Length

2023-08-07T00:14:56.046687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
deleted 188
 
5.2%
asksciencemoderator 43
 
1.2%
inquilinekea 8
 
0.2%
gargatua13013 6
 
0.2%
toorelad 6
 
0.2%
richardsonm24 5
 
0.1%
most-ant2788 5
 
0.1%
asachemicalengineer 5
 
0.1%
phealthy 5
 
0.1%
callmemateo 4
 
0.1%
Other values (2803) 3311
92.3%

Most occurring characters

ValueCountFrequency (%)
e 3477
 
8.5%
a 2952
 
7.2%
o 2535
 
6.2%
r 2430
 
6.0%
i 2208
 
5.4%
n 2180
 
5.4%
t 1971
 
4.8%
s 1724
 
4.2%
l 1541
 
3.8%
u 1133
 
2.8%
Other values (54) 18582
45.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31337
76.9%
Uppercase Letter 5820
 
14.3%
Decimal Number 2579
 
6.3%
Connector Punctuation 727
 
1.8%
Dash Punctuation 270
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3477
 
11.1%
a 2952
 
9.4%
o 2535
 
8.1%
r 2430
 
7.8%
i 2208
 
7.0%
n 2180
 
7.0%
t 1971
 
6.3%
s 1724
 
5.5%
l 1541
 
4.9%
u 1133
 
3.6%
Other values (16) 9186
29.3%
Uppercase Letter
ValueCountFrequency (%)
E 750
12.9%
D 602
 
10.3%
T 530
 
9.1%
S 423
 
7.3%
L 361
 
6.2%
A 358
 
6.2%
M 354
 
6.1%
C 266
 
4.6%
B 253
 
4.3%
R 204
 
3.5%
Other values (16) 1719
29.5%
Decimal Number
ValueCountFrequency (%)
1 419
16.2%
2 363
14.1%
0 284
11.0%
4 276
10.7%
9 267
10.4%
3 244
9.5%
7 222
8.6%
6 186
7.2%
8 166
 
6.4%
5 152
 
5.9%
Connector Punctuation
ValueCountFrequency (%)
_ 727
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37157
91.2%
Common 3576
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3477
 
9.4%
a 2952
 
7.9%
o 2535
 
6.8%
r 2430
 
6.5%
i 2208
 
5.9%
n 2180
 
5.9%
t 1971
 
5.3%
s 1724
 
4.6%
l 1541
 
4.1%
u 1133
 
3.0%
Other values (42) 15006
40.4%
Common
ValueCountFrequency (%)
_ 727
20.3%
1 419
11.7%
2 363
10.2%
0 284
 
7.9%
4 276
 
7.7%
- 270
 
7.6%
9 267
 
7.5%
3 244
 
6.8%
7 222
 
6.2%
6 186
 
5.2%
Other values (2) 318
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3477
 
8.5%
a 2952
 
7.2%
o 2535
 
6.2%
r 2430
 
6.0%
i 2208
 
5.4%
n 2180
 
5.4%
t 1971
 
4.8%
s 1724
 
4.2%
l 1541
 
3.8%
u 1133
 
2.8%
Other values (54) 18582
45.6%

score
Real number (ℝ)

Distinct1745
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4236.9389
Minimum1
Maximum83382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:56.208506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median150.5
Q37975.75
95-th percentile13936.5
Maximum83382
Range83381
Interquartile range (IQR)7967.75

Descriptive statistics

Standard deviation5790.3025
Coefficient of variation (CV)1.366624
Kurtosis16.362776
Mean4236.9389
Median Absolute Deviation (MAD)149.5
Skewness2.4165732
Sum15193663
Variance33527603
MonotonicityNot monotonic
2023-08-07T00:14:56.364573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 210
 
5.9%
2 151
 
4.2%
3 132
 
3.7%
4 106
 
3.0%
6 100
 
2.8%
5 96
 
2.7%
7 79
 
2.2%
8 64
 
1.8%
10 64
 
1.8%
11 62
 
1.7%
Other values (1735) 2522
70.3%
ValueCountFrequency (%)
1 210
5.9%
2 151
4.2%
3 132
3.7%
4 106
3.0%
5 96
2.7%
6 100
2.8%
7 79
 
2.2%
8 64
 
1.8%
9 54
 
1.5%
10 64
 
1.8%
ValueCountFrequency (%)
83382 1
< 0.1%
65835 1
< 0.1%
51927 1
< 0.1%
39288 1
< 0.1%
37681 1
< 0.1%
34365 1
< 0.1%
34357 1
< 0.1%
32564 1
< 0.1%
32562 1
< 0.1%
32346 1
< 0.1%

upvote_ratio
Real number (ℝ)

Distinct49
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80674568
Minimum0.5
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:56.522009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.53
Q10.7
median0.87
Q30.9275
95-th percentile0.95
Maximum1
Range0.5
Interquartile range (IQR)0.2275

Descriptive statistics

Standard deviation0.14600694
Coefficient of variation (CV)0.18098261
Kurtosis-0.82615547
Mean0.80674568
Median Absolute Deviation (MAD)0.07
Skewness-0.78830525
Sum2892.99
Variance0.021318026
MonotonicityNot monotonic
2023-08-07T00:14:56.682520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.94 276
 
7.7%
0.93 253
 
7.1%
0.92 233
 
6.5%
0.95 224
 
6.2%
0.91 193
 
5.4%
0.9 189
 
5.3%
0.89 139
 
3.9%
0.88 127
 
3.5%
0.52 108
 
3.0%
0.53 100
 
2.8%
Other values (39) 1744
48.6%
ValueCountFrequency (%)
0.5 1
 
< 0.1%
0.51 38
 
1.1%
0.52 108
3.0%
0.53 100
2.8%
0.54 75
2.1%
0.55 63
1.8%
0.56 63
1.8%
0.57 44
1.2%
0.58 37
 
1.0%
0.59 31
 
0.9%
ValueCountFrequency (%)
1 44
 
1.2%
0.97 5
 
0.1%
0.96 95
 
2.6%
0.95 224
6.2%
0.94 276
7.7%
0.93 253
7.1%
0.92 233
6.5%
0.91 193
5.4%
0.9 189
5.3%
0.89 139
3.9%

url
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3120
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
https://www.reddit.com/r/askscience/comments/7ft65h/if_light_can_travel_freely_through_space_why_isnt/
 
2
https://www.reddit.com/r/askscience/comments/74vncq/is_it_possible_to_put_my_bare_foot_on_the_moon/
 
2
https://www.reddit.com/r/askscience/comments/5s7ago/are_humans_closer_in_relative_size_to_the_planck/
 
2
https://www.reddit.com/r/askscience/comments/9w4iyv/what_is_the_ecological_impact_of_toilet_paper/
 
2
https://www.reddit.com/r/askscience/comments/fcu7ak/humans_seem_to_have_a_universally_visceral/
 
2
Other values (3115)
3576 

Length

Max length103
Median length100
Mean length97.809537
Min length39

Characters and Unicode

Total characters350745
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2654 ?
Unique (%)74.0%

Sample

1st rowhttps://www.reddit.com/r/askscience/comments/b863wm/where_in_your_body_does_your_food_turn_brown/
2nd rowhttps://www.reddit.com/r/askscience/comments/sn3a8e/is_the_omicron_variant_affecting_the_heart_like/
3rd rowhttps://www.reddit.com/r/askscience/comments/s3b34v/why_does_the_water_pull_back_before_a_tsunami/
4th rowhttps://www.reddit.com/r/askscience/comments/yteky2/why_do_isotopes_have_a_fractional_atomic_mass/
5th rowhttps://www.reddit.com/r/askscience/comments/15grw8/wolfram_alpha_says_that_the_uks_life_expectancy/

Common Values

ValueCountFrequency (%)
https://www.reddit.com/r/askscience/comments/7ft65h/if_light_can_travel_freely_through_space_why_isnt/ 2
 
0.1%
https://www.reddit.com/r/askscience/comments/74vncq/is_it_possible_to_put_my_bare_foot_on_the_moon/ 2
 
0.1%
https://www.reddit.com/r/askscience/comments/5s7ago/are_humans_closer_in_relative_size_to_the_planck/ 2
 
0.1%
https://www.reddit.com/r/askscience/comments/9w4iyv/what_is_the_ecological_impact_of_toilet_paper/ 2
 
0.1%
https://www.reddit.com/r/askscience/comments/fcu7ak/humans_seem_to_have_a_universally_visceral/ 2
 
0.1%
https://www.reddit.com/r/askscience/comments/5lrjgz/is_there_a_reason_all_the_planets_orbit_the_sun/ 2
 
0.1%
https://www.reddit.com/r/askscience/comments/c0w2xv/why_can_hormone_therapy_make_a_clitoris_grow_but/ 2
 
0.1%
https://www.reddit.com/r/askscience/comments/hrsap6/covid19_started_with_one_person_getting_infected/ 2
 
0.1%
https://www.reddit.com/r/askscience/comments/627akk/do_giraffes_get_struck_by_lightning_more_often/ 2
 
0.1%
https://www.reddit.com/r/askscience/comments/j1vdwu/why_are_garlic_and_onions_poisonous_to_dogs_and/ 2
 
0.1%
Other values (3110) 3566
99.4%

Length

2023-08-07T00:14:56.854253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.reddit.com/r/askscience/comments/7ft65h/if_light_can_travel_freely_through_space_why_isnt 2
 
0.1%
https://www.reddit.com/r/askscience/comments/7rxv4o/what_exactly_is_happening_to_your_nerves_when 2
 
0.1%
https://www.reddit.com/r/askscience/comments/9t3wnx/if_you_were_to_fall_down_a_skyscrapers_elevator 2
 
0.1%
https://www.reddit.com/r/askscience/comments/9frpsu/if_a_person_is_paralyzed_from_the_neck_down_does 2
 
0.1%
https://www.reddit.com/r/askscience/comments/o1zuxt/does_mars_have_caves_and_if_we_discover_a_large 2
 
0.1%
https://www.reddit.com/r/askscience/comments/9ssf5r/does_dyslexia_occur_in_blind_people_for_instance 2
 
0.1%
https://www.reddit.com/r/askscience/comments/aqyjdm/if_for_some_reason_you_have_a_handful_of_feces_in 2
 
0.1%
https://www.reddit.com/r/askscience/comments/5l7nj4/if_we_could_drain_the_ocean_could_we_breath_or 2
 
0.1%
https://www.reddit.com/r/askscience/comments/gtdmys/what_is_the_diameter_of_a_lightning_they_are 2
 
0.1%
https://www.reddit.com/r/askscience/comments/9j1us0/have_humans_always_had_an_all_year_round_mating 2
 
0.1%
Other values (3110) 3566
99.4%

Most occurring characters

ValueCountFrequency (%)
e 31512
 
9.0%
/ 28685
 
8.2%
_ 26472
 
7.5%
t 25958
 
7.4%
s 24609
 
7.0%
c 19541
 
5.6%
o 18452
 
5.3%
i 17292
 
4.9%
n 16386
 
4.7%
a 15627
 
4.5%
Other values (31) 126211
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 278176
79.3%
Other Punctuation 39443
 
11.2%
Connector Punctuation 26472
 
7.5%
Decimal Number 6654
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 31512
11.3%
t 25958
 
9.3%
s 24609
 
8.8%
c 19541
 
7.0%
o 18452
 
6.6%
i 17292
 
6.2%
n 16386
 
5.9%
a 15627
 
5.6%
r 15453
 
5.6%
w 15265
 
5.5%
Other values (17) 78081
28.1%
Decimal Number
ValueCountFrequency (%)
3 710
10.7%
1 710
10.7%
8 700
10.5%
9 694
10.4%
7 693
10.4%
2 677
10.2%
6 632
9.5%
0 614
9.2%
5 614
9.2%
4 610
9.2%
Other Punctuation
ValueCountFrequency (%)
/ 28685
72.7%
. 7172
 
18.2%
: 3586
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 26472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 278176
79.3%
Common 72569
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 31512
11.3%
t 25958
 
9.3%
s 24609
 
8.8%
c 19541
 
7.0%
o 18452
 
6.6%
i 17292
 
6.2%
n 16386
 
5.9%
a 15627
 
5.6%
r 15453
 
5.6%
w 15265
 
5.5%
Other values (17) 78081
28.1%
Common
ValueCountFrequency (%)
/ 28685
39.5%
_ 26472
36.5%
. 7172
 
9.9%
: 3586
 
4.9%
3 710
 
1.0%
1 710
 
1.0%
8 700
 
1.0%
9 694
 
1.0%
7 693
 
1.0%
2 677
 
0.9%
Other values (4) 2470
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 350744
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 31512
 
9.0%
/ 28685
 
8.2%
_ 26472
 
7.5%
t 25958
 
7.4%
s 24609
 
7.0%
c 19541
 
5.6%
o 18452
 
5.3%
i 17292
 
4.9%
n 16386
 
4.7%
a 15627
 
4.5%
Other values (30) 126210
36.0%
None
ValueCountFrequency (%)
ö 1
100.0%

num_questions_title
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1678751
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:56.986233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44594538
Coefficient of variation (CV)0.38184339
Kurtosis15.302009
Mean1.1678751
Median Absolute Deviation (MAD)0
Skewness3.2980802
Sum4188
Variance0.19886728
MonotonicityNot monotonic
2023-08-07T00:14:57.100869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 3067
85.5%
2 452
 
12.6%
3 57
 
1.6%
4 5
 
0.1%
5 4
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
1 3067
85.5%
2 452
 
12.6%
3 57
 
1.6%
4 5
 
0.1%
5 4
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 4
 
0.1%
4 5
 
0.1%
3 57
 
1.6%
2 452
 
12.6%
1 3067
85.5%

num_questions_body
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1385945
Minimum0
Maximum14
Zeros1531
Zeros (%)42.7%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:57.223555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4250321
Coefficient of variation (CV)1.2515712
Kurtosis8.1943078
Mean1.1385945
Median Absolute Deviation (MAD)1
Skewness2.1212569
Sum4083
Variance2.0307165
MonotonicityNot monotonic
2023-08-07T00:14:57.336328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 1531
42.7%
1 965
26.9%
2 592
 
16.5%
3 278
 
7.8%
4 117
 
3.3%
5 57
 
1.6%
6 21
 
0.6%
8 8
 
0.2%
7 6
 
0.2%
9 5
 
0.1%
Other values (5) 6
 
0.2%
ValueCountFrequency (%)
0 1531
42.7%
1 965
26.9%
2 592
 
16.5%
3 278
 
7.8%
4 117
 
3.3%
5 57
 
1.6%
6 21
 
0.6%
7 6
 
0.2%
8 8
 
0.2%
9 5
 
0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 2
 
0.1%
9 5
 
0.1%
8 8
 
0.2%
7 6
 
0.2%
6 21
 
0.6%
5 57
1.6%

num_cite
Real number (ℝ)

Distinct15
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15476854
Minimum0
Maximum19
Zeros3342
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:57.455423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.87505385
Coefficient of variation (CV)5.6539515
Kurtosis163.31603
Mean0.15476854
Median Absolute Deviation (MAD)0
Skewness10.943614
Sum555
Variance0.76571924
MonotonicityNot monotonic
2023-08-07T00:14:57.575423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 3342
93.2%
1 137
 
3.8%
2 49
 
1.4%
3 22
 
0.6%
4 12
 
0.3%
6 6
 
0.2%
5 4
 
0.1%
10 4
 
0.1%
7 3
 
0.1%
9 2
 
0.1%
Other values (5) 5
 
0.1%
ValueCountFrequency (%)
0 3342
93.2%
1 137
 
3.8%
2 49
 
1.4%
3 22
 
0.6%
4 12
 
0.3%
5 4
 
0.1%
6 6
 
0.2%
7 3
 
0.1%
8 1
 
< 0.1%
9 2
 
0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
17 1
 
< 0.1%
15 1
 
< 0.1%
12 1
 
< 0.1%
10 4
0.1%
9 2
 
0.1%
8 1
 
< 0.1%
7 3
0.1%
6 6
0.2%
5 4
0.1%

author_kind
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0
3355 
1
 
188
2
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3586
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3355
93.6%
1 188
 
5.2%
2 43
 
1.2%

Length

2023-08-07T00:14:57.708266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T00:14:57.845274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3355
93.6%
1 188
 
5.2%
2 43
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 3355
93.6%
1 188
 
5.2%
2 43
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3586
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3355
93.6%
1 188
 
5.2%
2 43
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3586
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3355
93.6%
1 188
 
5.2%
2 43
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3355
93.6%
1 188
 
5.2%
2 43
 
1.2%

year
Real number (ℝ)

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.6475
Minimum2010
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:57.944984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2014
Q12018
median2021
Q32022
95-th percentile2022
Maximum2022
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.853096
Coefficient of variation (CV)0.0014126703
Kurtosis0.62807355
Mean2019.6475
Median Absolute Deviation (MAD)1
Skewness-1.1554806
Sum7242456
Variance8.140157
MonotonicityNot monotonic
2023-08-07T00:14:58.078877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2022 1650
46.0%
2018 360
 
10.0%
2020 353
 
9.8%
2017 309
 
8.6%
2019 279
 
7.8%
2021 169
 
4.7%
2016 128
 
3.6%
2015 92
 
2.6%
2014 71
 
2.0%
2012 67
 
1.9%
Other values (3) 108
 
3.0%
ValueCountFrequency (%)
2010 1
 
< 0.1%
2011 57
 
1.6%
2012 67
 
1.9%
2013 50
 
1.4%
2014 71
 
2.0%
2015 92
 
2.6%
2016 128
 
3.6%
2017 309
8.6%
2018 360
10.0%
2019 279
7.8%
ValueCountFrequency (%)
2022 1650
46.0%
2021 169
 
4.7%
2020 353
 
9.8%
2019 279
 
7.8%
2018 360
 
10.0%
2017 309
 
8.6%
2016 128
 
3.6%
2015 92
 
2.6%
2014 71
 
2.0%
2013 50
 
1.4%

month
Real number (ℝ)

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.133575
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:58.199983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.9488013
Coefficient of variation (CV)0.64380092
Kurtosis-1.5805461
Mean6.133575
Median Absolute Deviation (MAD)4
Skewness0.10024307
Sum21995
Variance15.593031
MonotonicityNot monotonic
2023-08-07T00:14:58.311000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 554
15.4%
11 552
15.4%
1 487
13.6%
10 358
10.0%
3 351
9.8%
12 251
7.0%
8 193
 
5.4%
4 181
 
5.0%
7 172
 
4.8%
6 168
 
4.7%
Other values (2) 319
8.9%
ValueCountFrequency (%)
1 487
13.6%
2 554
15.4%
3 351
9.8%
4 181
 
5.0%
5 167
 
4.7%
6 168
 
4.7%
7 172
 
4.8%
8 193
 
5.4%
9 152
 
4.2%
10 358
10.0%
ValueCountFrequency (%)
12 251
7.0%
11 552
15.4%
10 358
10.0%
9 152
 
4.2%
8 193
 
5.4%
7 172
 
4.8%
6 168
 
4.7%
5 167
 
4.7%
4 181
 
5.0%
3 351
9.8%

day
Real number (ℝ)

Distinct31
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.467652
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:58.432896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8319622
Coefficient of variation (CV)0.57099566
Kurtosis-1.1809473
Mean15.467652
Median Absolute Deviation (MAD)8
Skewness0.063214525
Sum55467
Variance78.003556
MonotonicityNot monotonic
2023-08-07T00:14:58.567581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
12 155
 
4.3%
1 139
 
3.9%
7 136
 
3.8%
15 132
 
3.7%
8 130
 
3.6%
26 129
 
3.6%
13 125
 
3.5%
23 125
 
3.5%
27 122
 
3.4%
3 121
 
3.4%
Other values (21) 2272
63.4%
ValueCountFrequency (%)
1 139
3.9%
2 105
2.9%
3 121
3.4%
4 121
3.4%
5 119
3.3%
6 118
3.3%
7 136
3.8%
8 130
3.6%
9 107
3.0%
10 113
3.2%
ValueCountFrequency (%)
31 78
2.2%
30 113
3.2%
29 102
2.8%
28 98
2.7%
27 122
3.4%
26 129
3.6%
25 84
2.3%
24 121
3.4%
23 125
3.5%
22 116
3.2%

title_len
Real number (ℝ)

Distinct254
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.863079
Minimum14
Maximum299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:58.723821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile36
Q158
median85
Q3121
95-th percentile202
Maximum299
Range285
Interquartile range (IQR)63

Descriptive statistics

Standard deviation52.332234
Coefficient of variation (CV)0.54027019
Kurtosis1.6414075
Mean96.863079
Median Absolute Deviation (MAD)30
Skewness1.2559995
Sum347351
Variance2738.6627
MonotonicityNot monotonic
2023-08-07T00:14:58.881178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 49
 
1.4%
55 48
 
1.3%
60 47
 
1.3%
51 44
 
1.2%
78 43
 
1.2%
83 43
 
1.2%
88 41
 
1.1%
66 41
 
1.1%
53 41
 
1.1%
58 40
 
1.1%
Other values (244) 3149
87.8%
ValueCountFrequency (%)
14 1
 
< 0.1%
18 3
 
0.1%
19 2
 
0.1%
20 5
0.1%
21 5
0.1%
22 4
0.1%
23 5
0.1%
24 8
0.2%
25 4
0.1%
26 9
0.3%
ValueCountFrequency (%)
299 4
0.1%
298 1
 
< 0.1%
297 3
0.1%
296 1
 
< 0.1%
295 3
0.1%
294 3
0.1%
292 3
0.1%
291 1
 
< 0.1%
290 1
 
< 0.1%
287 2
0.1%

body_len
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct856
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.01701
Minimum0
Maximum5134
Zeros1531
Zeros (%)42.7%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:59.043132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median100.5
Q3334
95-th percentile896
Maximum5134
Range5134
Interquartile range (IQR)334

Descriptive statistics

Standard deviation420.45538
Coefficient of variation (CV)1.744505
Kurtosis33.92663
Mean241.01701
Median Absolute Deviation (MAD)100.5
Skewness4.6146839
Sum864287
Variance176782.73
MonotonicityNot monotonic
2023-08-07T00:14:59.202659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1531
42.7%
139 10
 
0.3%
296 9
 
0.3%
105 8
 
0.2%
172 8
 
0.2%
334 8
 
0.2%
116 8
 
0.2%
96 8
 
0.2%
217 8
 
0.2%
110 8
 
0.2%
Other values (846) 1980
55.2%
ValueCountFrequency (%)
0 1531
42.7%
1 2
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 3
 
0.1%
10 1
 
< 0.1%
13 2
 
0.1%
14 3
 
0.1%
16 1
 
< 0.1%
17 2
 
0.1%
ValueCountFrequency (%)
5134 1
< 0.1%
5003 1
< 0.1%
4515 1
< 0.1%
4431 1
< 0.1%
4233 2
0.1%
4190 1
< 0.1%
4125 1
< 0.1%
4104 1
< 0.1%
3991 1
< 0.1%
3699 1
< 0.1%

tag_index
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5462911
Minimum0
Maximum29
Zeros412
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:59.345091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q39
95-th percentile17
Maximum29
Range29
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.3566996
Coefficient of variation (CV)0.81828007
Kurtosis0.23951044
Mean6.5462911
Median Absolute Deviation (MAD)4
Skewness0.90846947
Sum23475
Variance28.69423
MonotonicityNot monotonic
2023-08-07T00:14:59.479502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 639
17.8%
2 532
14.8%
0 412
11.5%
3 298
8.3%
8 278
7.8%
9 231
 
6.4%
15 196
 
5.5%
7 175
 
4.9%
13 167
 
4.7%
11 116
 
3.2%
Other values (19) 542
15.1%
ValueCountFrequency (%)
0 412
11.5%
1 2
 
0.1%
2 532
14.8%
3 298
8.3%
4 639
17.8%
5 96
 
2.7%
6 47
 
1.3%
7 175
 
4.9%
8 278
7.8%
9 231
 
6.4%
ValueCountFrequency (%)
29 1
 
< 0.1%
27 1
 
< 0.1%
26 12
 
0.3%
25 2
 
0.1%
24 12
 
0.3%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 2
 
0.1%
20 1
 
< 0.1%
19 72
2.0%

edit
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013385388
Minimum0
Maximum14
Zeros3560
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:14:59.599711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.26688842
Coefficient of variation (CV)19.938789
Kurtosis2127.2198
Mean0.013385388
Median Absolute Deviation (MAD)0
Skewness42.219505
Sum48
Variance0.071229429
MonotonicityNot monotonic
2023-08-07T00:14:59.703760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 3560
99.3%
1 19
 
0.5%
2 4
 
0.1%
14 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 3560
99.3%
1 19
 
0.5%
2 4
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 4
 
0.1%
1 19
 
0.5%
0 3560
99.3%

bold_B
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
0
3585 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3585
> 99.9%
1 1
 
< 0.1%

Length

2023-08-07T00:14:59.842470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T00:14:59.971547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3585
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 3585
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3586
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3585
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3586
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3585
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3585
> 99.9%
1 1
 
< 0.1%

nice
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07334077
Minimum0
Maximum5
Zeros3368
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:15:00.062408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32544846
Coefficient of variation (CV)4.4374836
Kurtosis57.983823
Mean0.07334077
Median Absolute Deviation (MAD)0
Skewness6.3481164
Sum263
Variance0.1059167
MonotonicityNot monotonic
2023-08-07T00:15:00.169645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 3368
93.9%
1 188
 
5.2%
2 21
 
0.6%
3 5
 
0.1%
5 2
 
0.1%
4 2
 
0.1%
ValueCountFrequency (%)
0 3368
93.9%
1 188
 
5.2%
2 21
 
0.6%
3 5
 
0.1%
4 2
 
0.1%
5 2
 
0.1%
ValueCountFrequency (%)
5 2
 
0.1%
4 2
 
0.1%
3 5
 
0.1%
2 21
 
0.6%
1 188
 
5.2%
0 3368
93.9%

upvote_bins
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.652538
Minimum0
Maximum19
Zeros147
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:15:00.289311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median14
Q316.75
95-th percentile17
Maximum19
Range19
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation5.8341815
Coefficient of variation (CV)0.50067905
Kurtosis-0.83811815
Mean11.652538
Median Absolute Deviation (MAD)3
Skewness-0.78616029
Sum41786
Variance34.037674
MonotonicityNot monotonic
2023-08-07T00:15:00.418983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
17 753
21.0%
15 455
12.7%
16 426
11.9%
1 238
 
6.6%
14 180
 
5.0%
13 177
 
4.9%
0 147
 
4.1%
11 128
 
3.6%
9 125
 
3.5%
3 113
 
3.2%
Other values (10) 844
23.5%
ValueCountFrequency (%)
0 147
4.1%
1 238
6.6%
2 107
3.0%
3 113
3.2%
4 49
 
1.4%
5 104
2.9%
6 78
 
2.2%
7 93
 
2.6%
8 79
 
2.2%
9 125
3.5%
ValueCountFrequency (%)
19 44
 
1.2%
18 100
 
2.8%
17 753
21.0%
16 426
11.9%
15 455
12.7%
14 180
 
5.0%
13 177
 
4.9%
12 98
 
2.7%
11 128
 
3.6%
10 92
 
2.6%

score_bins
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9135527
Minimum0
Maximum14
Zeros361
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2023-08-07T00:15:00.550232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile14
Maximum14
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.4099925
Coefficient of variation (CV)0.63787646
Kurtosis-1.2195968
Mean6.9135527
Median Absolute Deviation (MAD)4
Skewness-0.016277514
Sum24792
Variance19.448034
MonotonicityNot monotonic
2023-08-07T00:15:00.668733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 361
 
10.1%
2 302
 
8.4%
5 254
 
7.1%
14 241
 
6.7%
12 240
 
6.7%
7 239
 
6.7%
11 238
 
6.6%
13 237
 
6.6%
9 237
 
6.6%
10 236
 
6.6%
Other values (5) 1001
27.9%
ValueCountFrequency (%)
0 361
10.1%
1 132
 
3.7%
2 302
8.4%
3 197
5.5%
4 212
5.9%
5 254
7.1%
6 226
6.3%
7 239
6.7%
8 234
6.5%
9 237
6.6%
ValueCountFrequency (%)
14 241
6.7%
13 237
6.6%
12 240
6.7%
11 238
6.6%
10 236
6.6%
9 237
6.6%
8 234
6.5%
7 239
6.7%
6 226
6.3%
5 254
7.1%

Interactions

2023-08-07T00:14:52.121048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:20.168773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:22.079320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:24.605908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:26.569420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:28.551520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:30.655597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:32.789620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:34.821560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:36.914888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:39.030442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:41.039211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:42.983962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:45.788095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:47.888010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:49.924673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:52.248414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:20.281409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:22.194425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:24.724064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:26.682109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:28.684749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:30.802249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:32.916614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:34.944205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:37.030907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:39.153311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:41.150422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:43.103197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:45.910573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:48.009902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:50.045020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:52.381101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:20.390693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:22.305724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:24.834315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:26.801982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:28.804974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:30.931622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:33.037790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:35.062627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:37.145761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:39.269260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:41.265733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:43.225890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:46.026190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:48.130519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:50.170953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:52.517775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:20.505085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:22.427161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:24.950303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:26.930737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:28.931949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:31.072855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:33.162863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:35.268681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:37.273300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:39.385698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:41.383906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:43.346319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:46.145506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:48.262784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:50.310138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:52.650255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:20.617910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:22.546126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:25.073225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:27.051460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:29.099559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:31.198624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:33.291270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:35.435522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:37.392580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:39.510923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:41.503551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:43.471446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:46.271420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:48.392882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:50.445974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:52.778060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:20.734956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:22.670354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:25.204820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:27.180332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:29.235067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:31.333453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:33.416960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:35.562242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:37.557664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:39.639683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:41.626732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:43.601319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:46.416604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:48.523084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:50.579004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:52.913632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:20.857033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:22.789640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:25.332857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:27.307958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:29.366181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:31.472077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:33.547523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:35.685470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:37.730463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:39.774398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:41.751467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:43.724654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:46.551738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:48.655203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:50.711544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:53.037543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:20.980951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:22.909834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:25.455982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:27.428881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:29.495288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:31.600259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:33.674741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:35.812014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:37.852788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:39.907025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:41.893956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:43.852269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:46.682162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:48.778269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:50.844360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:53.167537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:21.097172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:23.027323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:25.580127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:27.552384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:29.620095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:31.746961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:33.796948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:35.927686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:37.966516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:40.028853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:42.009221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:43.972655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:46.812811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:48.902227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:50.974937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:53.300027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:21.212113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:23.144632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:25.701867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:27.676094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:29.746898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:31.877964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:33.930625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:36.047084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:38.092494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:40.148756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:42.135783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:44.089073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:46.947065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:49.023903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:51.113115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:53.434792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:21.332350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:23.913076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:25.826032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:27.842124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:29.873993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:32.007280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:34.049479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:36.170121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:38.222104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:40.277757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:42.257884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:44.209857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:47.077018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:49.149402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:51.257391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:53.573213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:21.448933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:24.021547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:25.946609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:27.958665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:30.001282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:32.137945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:34.167791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:36.284716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:38.337859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:40.394514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:42.373081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:44.332459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:47.202188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:49.277975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:51.392324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:53.701552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:21.567333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:24.135646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:26.071098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:28.075846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:30.129058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:32.270054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:34.302695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:36.406577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:38.507503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:40.523361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:42.496296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:44.453280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:47.337388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:49.403762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:51.529589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:53.841604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:21.703119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:24.250967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:26.201460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:28.195149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:30.256684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:32.402265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:34.429739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:36.536598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:38.657727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:40.661978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:42.617335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:44.579503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:47.480403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:49.528331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:51.668718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:53.970296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:21.818795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:24.362052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:26.321132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:28.309038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:30.388275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:32.525152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:34.555978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:36.661994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:38.771428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:40.786299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:42.734385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:44.709694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:47.615281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:49.655006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:51.794357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:54.113480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:21.951615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:24.480403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:26.447935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:28.429511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:30.516738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:32.659280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:34.690773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:36.789560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:38.896177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:40.908662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:42.863641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:45.658359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:47.754455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:49.791504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-07T00:14:51.948883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-08-07T00:15:00.812697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0scoreupvote_rationum_questions_titlenum_questions_bodynum_citeyearmonthdaytitle_lenbody_lentag_indexeditniceupvote_binsscore_binstagauthor_kindbold_B
Unnamed: 01.000-0.464-0.258-0.0630.073-0.0510.355-0.508-0.009-0.1400.074-0.004-0.060-0.092-0.259-0.4650.1020.1420.000
score-0.4641.0000.7380.113-0.339-0.015-0.3620.0620.0280.257-0.322-0.0480.0470.0460.7400.9980.1890.1500.000
upvote_ratio-0.2580.7381.0000.079-0.269-0.025-0.0050.0020.0420.239-0.249-0.087-0.0120.0170.9940.7360.0980.0970.000
num_questions_title-0.0630.1130.0791.000-0.133-0.030-0.0840.0330.0090.338-0.146-0.0110.012-0.0050.0800.1120.0000.0000.000
num_questions_body0.073-0.339-0.269-0.1331.0000.2360.223-0.0380.026-0.2720.8950.0270.0860.246-0.266-0.3370.0000.0850.000
num_cite-0.051-0.015-0.025-0.0300.2361.000-0.0030.0050.0490.0280.3440.0380.0170.119-0.022-0.0160.3680.3260.000
year0.355-0.362-0.005-0.0840.223-0.0031.000-0.127-0.011-0.1230.244-0.116-0.077-0.056-0.008-0.3650.1620.1570.007
month-0.5080.0620.0020.033-0.0380.005-0.1271.000-0.0440.037-0.0430.0270.0040.007-0.0020.0630.0870.0480.000
day-0.0090.0280.0420.0090.0260.049-0.011-0.0441.0000.0210.035-0.0190.0030.0240.0400.0270.0440.0000.000
title_len-0.1400.2570.2390.338-0.2720.028-0.1230.0370.0211.000-0.224-0.024-0.0200.0000.2370.2550.0770.0760.000
body_len0.074-0.322-0.249-0.1460.8950.3440.244-0.0430.035-0.2241.0000.0220.1090.274-0.245-0.3210.2600.2850.000
tag_index-0.004-0.048-0.087-0.0110.0270.038-0.1160.027-0.019-0.0240.0221.0000.0050.005-0.089-0.0470.9970.0830.031
edit-0.0600.047-0.0120.0120.0860.017-0.0770.0040.003-0.0200.1090.0051.0000.244-0.0130.0470.0000.0000.000
nice-0.0920.0460.017-0.0050.2460.119-0.0560.0070.0240.0000.2740.0050.2441.0000.0200.0480.0830.0400.060
upvote_bins-0.2590.7400.9940.080-0.266-0.022-0.008-0.0020.0400.237-0.245-0.089-0.0130.0201.0000.7380.0980.0970.000
score_bins-0.4650.9980.7360.112-0.337-0.016-0.3650.0630.0270.255-0.321-0.0470.0470.0480.7381.0000.0870.0900.038
tag0.1020.1890.0980.0000.0000.3680.1620.0870.0440.0770.2600.9970.0000.0830.0980.0871.0000.1770.000
author_kind0.1420.1500.0970.0000.0850.3260.1570.0480.0000.0760.2850.0830.0000.0400.0970.0900.1771.0000.000
bold_B0.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0310.0000.0600.0000.0380.0000.0001.000

Missing values

2023-08-07T00:14:54.331940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-07T00:14:54.697989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-07T00:14:54.893078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0titlebodytagdatetimeauthorscoreupvote_ratiourlnum_questions_titlenum_questions_bodynum_citeauthor_kindyearmonthdaytitle_lenbody_lentag_indexeditbold_Bniceupvote_binsscore_bins
285285Where in your body does your food turn brown?I know this is maybe a stupid question, but poop is brown, but when you throw up your throw up is just the color of your food. Where does your body make your food brown? (Sorry for my crappy English)\n\nEdit: Thank you guys so much for the anwers and thanks dor the gold. This post litteraly started by a friend and me just joking around. Thankshuman body2019-04-01 10:59:58Ayko03108270.92https://www.reddit.com/r/askscience/comments/b863wm/where_in_your_body_does_your_food_turn_brown/12002019414534300031613
37263726Is the Omicron variant affecting the heart like the earlier COVID variants were? Are cardiac effects less likely if one is vaccinated?NaNcovid-192022-02-07 14:39:54uoflcards2260.66https://www.reddit.com/r/askscience/comments/sn3a8e/is_the_omicron_variant_affecting_the_heart_like/20002022271340700062
41884188Why does the water pull back before a tsunami?NaNearth sciences2022-01-13 14:08:14BiLeftHanded30.62https://www.reddit.com/r/askscience/comments/s3b34v/why_does_the_water_pull_back_before_a_tsunami/10002022113460800041
13361336Why do isotopes have a fractional atomic mass?As the question asks, why do specific isotopes not have a whole number atomic mass? For instance, Ag-109 has an atomic mass of 108.9047558 amu and not just 109. Or Ag-107 has a mass of 106.9050915 amu.chemistry2022-11-12 10:23:09MSPaintIsBetter60.88https://www.reddit.com/r/askscience/comments/yteky2/why_do_isotopes_have_a_fractional_atomic_mass/1200202211124620115000152
25082508Wolfram Alpha says that the UK's life expectancy for men is 77 years. It then says that the chance of living past 80 is 51%. So if most people will live past 80, how can the life expectancy be less?Source: http://www.wolframalpha.com/input/?i=uk+life+expectancy+for+men&dataset=mathematics2012-12-26 04:30:15Briggykins20.53https://www.reddit.com/r/askscience/comments/15grw8/wolfram_alpha_says_that_the_uks_life_expectancy/121020121226198801900010
12401240When a military helicopter fires thousands of rounds while hovering still, does the operator have to slow the rotor to compensate for weight loss?NaNphysics2022-11-21 04:42:50Legitimate-BurnerAcc810.81https://www.reddit.com/r/askscience/comments/z0y2dl/when_a_military_helicopter_fires_thousands_of/10002022112114602000127
39973997How do chemists predict chemical compositions and their properties?How do chemists predict how a chemical reaction or product will behave, I understand how and why molecules combine themselves in the structures they end up in (like H2O for example) I also understand things like the acidity, charge and reactivity of the different elements. But the thing I can't wrap my head around is how we know about the ways a certain molecule will behave, take for example table salt, it is composed of 2 individually dangerous elements but the molecule they produce in this case is completely harmless.\nSo how do chemists predict how these compositions behave.\nI am a aspiring material / composite engineer (mostly self taught)chemistry2022-01-24 02:43:21Mandoart-Studios110.87https://www.reddit.com/r/askscience/comments/sbje6x/how_do_chemists_predict_chemical_compositions_and/110020221246765015000144
23392339Is our gravity here on Earth determined by the planet's mass, or its rotational speed?If the force put on us by the spinning Earth is similar to that of a centrifuge, is the gravitational force we feel coming from that spinning, or the mass of the Earth itself?physics2016-02-06 12:12:30FinestInPullman10.53https://www.reddit.com/r/askscience/comments/44hwaw/is_our_gravity_here_on_earth_determined_by_the/110020162686175200010
474474How worried should we be about the Clathrate Gun?[Clathrate gun](https://en.wikipedia.org/wiki/Clathrate_gun_hypothesis)\n\nYear after year is becoming hotter than the last. \n\nScientists are being ['caught off-guard'](http://www.reuters.com/article/us-weather-climatechange-science-idUSKCN1061RH?rpc=401) by record temperatures. \n\nNatalia Shakhova [says](https://www.youtube.com/watch?v=kx1Jxk6kjbQ) we may have only *DECADES* before things get really bad.\n\n[This thread](https://www.reddit.com/r/worldnews/comments/4ur3qh/scientists_caught_offguard_by_record_temperatures/) yesterday really scared the shit out of me. Are things really this dire? Could the human race be gone in less than 100 years?earth sciences2016-07-27 07:08:31claxius89320.82https://www.reddit.com/r/askscience/comments/4uus54/how_worried_should_we_be_about_the_clathrate_gun/144020167274964980001212
304304If you were to sky-dive in the rain, would water hit your stomach, back, or both?NaNphysics2018-12-04 07:16:58LEGSwhodoyoustandfor105140.93https://www.reddit.com/r/askscience/comments/a31jrd/if_you_were_to_skydive_in_the_rain_would_water/1000201812481020001713
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33713371When I ignite methanol, is it only the fumes that's on fire, or is it the liquid burning itself?NaNchemistry2022-03-05 03:25:29Ludvik_Pytlicek80.83https://www.reddit.com/r/askscience/comments/t77ih8/when_i_ignite_methanol_is_it_only_the_fumes_thats/100020223596015000133
24992499Are 1.000...001 and 1.000...009 different numbers?So I get that .999... and 1.000... are both equal to 1, but I got to thinking. Would something like 1.00..001 and 1.00..009, even though the 1 and the 9 are both arbitrarily small, have any kind of appreciable differences?\n\nDoes it even make sense to put some kind of terminating value following infinitely many 0s?mathematics2017-03-11 06:06:12BabiesDrivingGoKarts40.56https://www.reddit.com/r/askscience/comments/5yssi2/are_1000001_and_1000009_different_numbers/12002017311503151900022
28682868When eyeballs are donated by an organ donor, does the left eyeball have to be put in the left eye socket of the new body, and vice versa?NaNhuman body2021-11-19 05:44:50skyebullock110170.95https://www.reddit.com/r/askscience/comments/qxgi1o/when_eyeballs_are_donated_by_an_organ_donor_does/100020211119137000001713
13531353How did the Rocky Mountains form?How did the Rockies form so far inland? I’ve heard conflicting theories. Either there is a fault in the plates right around the Rocky’s foothills, or the Pacific plate went underneath the North American plate and pushed up underneath like an ingrown hair. Someone in geology please point me in the right direction.earth sciences2022-11-13 06:37:13RedStarBenny888860.77https://www.reddit.com/r/askscience/comments/yu3ff9/how_did_the_rocky_mountains_form/120020221113333148000107
32633263is it Known exactly which wolf/canine species modern dogs were bred fromand what region this occurred in?NaNbiology2022-03-12 12:04:23Mahxiac100.86https://www.reddit.com/r/askscience/comments/tcply5/is_it_known_exactly_which_wolfcanine_species/1000202231210504000144
32593259How do smoking cessation medications decrease withdrawal effects?I have looked into the mechanism of action for smoking cessation medications like Chantix and Bupropion. They are stated to be efficacious by binding to the α4β2 sub-type of the nicotinic receptor where it produces agonist activity, while simultaneously preventing nicotine binding to these receptors. \n\nThe part that confuses me is how it decreases the withdrawal effects. If the medication is acting as an agonist to nicotine receptors to block the dopamine system stimulation provided by nicotine — wouldn’t you still experience the same withdrawal by the lack of dopamine stimulation that you would experience by quitting without the help of cessation aids?\n\nTl;Dr: Based on my understanding of the mechanism, it seems that medications would ‘discourage’ smoking as it would remove the dopamine stimulation that smoking would give you, but doesn’t explain how it would decrease withdrawal effects.medicine2022-03-12 19:13:11tatro3660.80https://www.reddit.com/r/askscience/comments/tcxq9q/how_do_smoking_cessation_medications_decrease/12002022312659013000112
630630Is there any validity to the claim that Epsom salts "Increase the relaxing effects of a warm bath after strenuous exertion"? If so, what is the Underlying mechanism for this effect?This claim is printed in wide type on this box of ES we've got & my baloney detector is tingling.\n\nEDIT/UPDATE: Just a reminder to please remain on topic and refrain from anecdotal evidence and hearsay. If you have relevant expertise and can back up what you say with peer-reviewed literature, that's fine. Side-discussions about recreational drug use, effects on buoyancy, sensory deprivation tanks and just plain old off topic ramblings, while possibly very interesting, are being pruned off as off-topic, as per sub policy.\n\nSo far, what I'm taking of this is that there exists some literature claiming that some of the magnesium might be absorbed through the skin (thank you user /u/locused), but that whether that claim is credible or not, or whether the amounts are sufficient to have an effect is debatable or yet to be proven, as pointed out by several other users.medicine2017-04-17 07:43:22Gargatua1301379770.87https://www.reddit.com/r/askscience/comments/65vy4z/is_there_any_validity_to_the_claim_that_epsom/2100201741718187330001411
16021602How hard were ancient arthropod exoskeletons?So from the human perspective, modern arthropod exoskeletons are quite weak. I can crush even relatively large insects without much effort. However, we know that hundreds of millions of years ago there existed giant arthropods. How hard would their exoskeletons have been? If I was transported back to the carboniferous and faced a giant centipede would I be able to do anything to its "armor?"\n\nI'm assuming there is a relationship between the volume of the creature and the thickness of the chitin, like the whole square-cube law thing, but I don't know nearly enough about it.paleontology2022-10-24 11:54:21TrillCozbey170.85https://www.reddit.com/r/askscience/comments/ycjdk0/how_hard_were_ancient_arthropod_exoskeletons/130020221024455796000135
373373How can a vessel contain 100M degrees celsius?This is within context of the KSTAR project, but I'm curious how a material can contain that much heat. \n\n100,000,000°c seems like an ABSURD amount of heat to contain.\n\nIs it strictly a feat of material science, or is there more at play? (chemical shielding, etc) \n\nhttps://phys.org/news/2020-12-korean-artificial-sun-world-sec-long.htmlengineering2020-12-26 07:43:50therealkevinard97850.94https://www.reddit.com/r/askscience/comments/kkkh6k/how_can_a_vessel_contain_100m_degrees_celsius/12102020122646337130001712
5151Why are Primates incapable of Human speech, while lesser animals such as Parrots can emulate Human speech?NaNbiology2018-01-06 14:00:18HBOTB2217140.86https://www.reddit.com/r/askscience/comments/7omaq1/why_are_primates_incapable_of_human_speech_while/1000201816106040001414